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Yesterday Federal Agricultural Minister Gerry Ritz uttered insane lies about dairy supply management:

I would make the argument that I don’t see those inflated prices, certainly, depending on where you buy,” Ritz told a joint news conference with Alberta Agriculture Minister Evan Berger and Saskatchewan Agriculture Minister Bob Bjornerud.

I received a flyer in my mailbox last night when I got back to my apartment and I opened it up and it’s from Canadian Tire. They’ve got four litres of milk for $4.19. That’s completely comparable to the American price that we’re always being beat up over.

Canadian Tire Econometrics aside, consumers are of course harmed by high prices driven by quantity restrictions. Click here to see a graph showing how much higher our prices are than the EU, US, or New Zealand (all of which all of which except New Zealand [*] also have some sort of supply management, Canada’s is just more severe).
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Antibiotic overuse causes great social harm yet is largely absent from public discussion of drug policy. There is a textbook external effect of an antibiotic prescription: the more antibiotics are used, the higher the risk we all face of resistant infections. As a result, there tends to be too much use of antibiotics. There have been ongoing efforts to reduce use of antibiotics, particularly in the context of treating respiratory infections, in part by educating GPs, the supply side of the relationship, on appropriate use.

In “Patient knowledge and antibiotic abuse: Evidence from an audit study in China” Janet Currie, Wanchuan Lin, and Wei Zhang consider the demand side of the relationship: what is the effect of patient knowledge on antibiotic use? Read the rest of this entry »

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Say you wish to estimate a model with a binary dependent variable. You recall that you ought not use OLS primarily because OLS will not bound your predicted values between zero and one. So you use a nonlinear variant, say, probit. But you also recall that it doesn’t matter much if you just use OLS and ignore the binary nature of your dependent variable so long as you are interested in estimating the effects of your covariates, not generating predicted values.
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Frances Woolley has posted some great tips on how to clean data in Stata. This post follows up with some tips on how to quickly and robustly estimate models as you vary specifications, and on how to get your results in a publication-ready form. The .do file described in this post can be downloaded by clicking here, you must change the extension from .doc to .do.

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A new working paper by Michael Luca estimates the effect of Yelp reviews on Seattle restaurant revenues. Disentangling causality here is difficult, as even if reviews have no effect on revenues we would expect to observe reviews and revenues both moving with changes in underlying relative quality. Luca exploits a quirk in the way Yelp presents information: average scores are reported rounded in 0.5 star bins on a 5 star scale. For example, underlying average scores of 2.76 and 3.24 are both reported as “3 stars,” but a good review which bumps the average up to 3.25 bumps the reported score up to 3.5 stars. The estimates show that Yelp reviews do have a substantial effect on revenues.
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Nice quote from EconJeff on propensity score matching. The idea that somehow matching buys you causality in a situation in which you’d snicker at the idea that OLS does seems to be distressingly common.

Matching is not a design or an identifying assumption. Rather, it is one of several estimators that can be use when assuming selection on observed variables or unconfoundedness (or ignorability, or conditional independence, or whatever else your particular discipline or sub-field happens to call it this week). The key to evaluating an analysis based on an assumption of selection on observed variables is a careful consideration of the set of conditioning variables used in the analysis to deal with the problem of non-random selection into treatment. Estimator choice, e.g. matching versus linear regression versus inverse propensity weighting, is not unimportant, and can be very important for specific data generating processes, but what really matters in general is the set of conditioning variables.


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